S98 Linking ensemble spread to forecast risk and confidence

Sunday, 22 January 2017
4E (Washington State Convention Center )
Amanda Penning, South Dakota School of Mines and Technology, Rapid City, SD; and M. Kern and W. Capehart

Ensemble forecasting is currently one of the more effective methods of estimating forecast variability.  Local and Regional ensemble forecasting allows the ability to collect a large number of possible forecast outcomes and can articulate forecast uncertainty but at a high computational cost. 

SDSMT has developed a Confidence Index (CI); an algorithm applied to a single forecast to determine risk of forecast error.  It is computationally less expensive compared to forecast ensembles.  When applied to a region CI detects the presence of weather features that can lead to forecast error and creates a “score” that can be associated with forecast risk

In this project we are working to determine if the national ensembles created by NOAA and CI forecast risk scores are comparable ways to assess forecast risk and if existing national and global forecast ensembles can be integrated into the CI algorithm to better predict overall forecast uncertainty and risk.

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